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A Nonparametric Method for Fitting a Straight Line to a Noisy Image
September 1989 (vol. 11 no. 9)
pp. 998-1001

In fitting a straight line to a noisy image, the least-squares method becomes highly unreliable either when the noise distribution is nonnormal or when it is contaminated by outliers. The authors propose a nonparametric method, the median of the intercepts, to overcome these difficulties. This method is free of assumptions about the noise distribution and insensitive to outliers, and it does not require quantization of the parameter space. Thus, unlike the Hough transform, its outcome does not depend on the bin size. The method is efficient and its implementation does not involve practical difficulties such as local minima or poor convergence of iterative procedures.

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Index Terms:
straight line fitting; picture processing; pattern recognition; nonparametric method; noisy image; noise distribution; least squares approximations; pattern recognition; picture processing
Citation:
B. Kamgar-Parsi, B. Kamgar-Parsi, N.S. Netanyahu, "A Nonparametric Method for Fitting a Straight Line to a Noisy Image," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 9, pp. 998-1001, Sept. 1989, doi:10.1109/34.35504
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